AI-Based Framework for Agile Project Management

With the recent trends towards automation, machine learning and artificial intelligence as it appears that just the application of AI can not transform the enterprise computing as now, it has the power to change project management as well. A well-known core concept with AI systems is that their predictions are only as amazing as their data. Artificial intelligence has revolutionized the flow of businesses making decisions and deploying their resources.

Over the past years, it has proven its worth by helping businesses to flourish in various industries. In the current times, organizations use AI to automate mundane tasks by making it possible what is once considered impossible. Let us look at how AI-powered project management can be implemented and what can be the various benefits to it.

Artificial Intelligence has transformed how every business functions and software development is not exempted from it. Machine learning has helped to accelerate the traditional software development lifecycle as artificial intelligence redefines how developers build products. Usually, software development requires that you specify what you want from the system before you create it. Let us look after some essential factors which are crucial before applying AI in project management.

Handles a bulk of data.

The mainstream task in which AI seems to excel when compared to humans is in processing bulks of data. There are limitless examples of how team members apply AI to their work and achieve the desired results in just a few days which consumed them months and months before.

Using AI for data analytics helps project managers to run their project plans smoothly as compared to past performance. Hence, if a project is long-term or has an average amount of data associated with it for another reason, then the AI software can be utilized to compile the data for making decisions to reach valuable conclusions after data mining.

Cuts down Project Cost.

Project management experts are always engaged in a battle to ensure that they don’t exceed the limits. Some research indicates that artificial intelligence project management interfaces help to keep costs at a manageable level in multiple ways.

It is estimated that more than 50 percent of a project manager’s time goes up in administrative tasks which can be figured out by the analysts. Artificial intelligence can also visualize data and illuminate bottlenecks for the processes that could stay hidden.

How to introduce ML Techniques in Agile Development?

It is a well-known fact that significant application components like software interfaces and data management still utilize regular software. Howsoever, you can too introduce ML techniques into your SLDC as given:

Code Assistance and Automatic Code Refactoring.

Most of the developer’s time is spent on debugging code, and documentation reading which can be changed by using ML as the developers can get quick feedback and recommendation based on the codebase by saving a lot of time. For instance – Java’s Codota and Python’s Kite.

At the same time, it is also essential to have a clean code as it makes a more natural collaboration. Refactoring becomes an extreme necessity as the maintenance of clean code can be challenging at times. To resolve this, machine learning is utilized to analyze the code for better performance.

Strategic Decisions and Precise Estimates.

Developers also spent more time debating the features and prioritizing the products. An artificial intelligence model which is trained with data from previous development projects can help to assess how an application performs in helping business leaders and teams to identify methods of less risk and more impact.

The software development field is better known for exceeding budgets and timelines. So, to make a reasonable estimate, it is crucial to have a deeper understanding of both the context and team which stands to be influential in predicting effort and budget.

Error Handling, Analytics, and Prototyping.

Coding assistants based on machine learning can help to identify patterns from previous data. The coding assistant will flag it if an engineer makes an error during software development. Hence, ML can be used to analyze logs and flag errors that need to be fixed which makes the software developer proactive in resolving errors.

It generally takes a month or years to convert business requirements into the best technology. However, machine learning is reducing development time by helping out every individual to develop technologies with little to less technical knowledge.

The Final Verdict

There’s no doubt in how artificial intelligence has proven to become the top business prosperity by leveraging them towards automating mundane tasks. By using artificial intelligence in software development helps to be out more business benefits. Keep Learning!

Stephanie Donahole is working as a Business Analyst at Tatvasoft Australia a .net and web development company in australia also specialized in software development. Her aim is to sharpen her analytical skills, deepening her data understanding and broaden her business knowledge in these years of her career.